No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by Shubhamsaboo · Agent Tool · ★ 139.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-llm-apps safe to install? View the security audit →
Awesome LLM Apps 100+ open-source AI agents, agent skills, and RAG apps. Hand-built, tested end-to-end, Apache-2.0. Clone it, ship it, sell it - 100% free and open-source Works with Claude, Gemini, GPT, DeepSeek, Llama, Qwen and other open-source models. Step-by-step tutorials on Unwind AI · Quick start · Browse all templates Project Graveyard Insurance Claim Live Agent Team <a href="advancedaiagents/singleagentapps/aifraudinve
| Stars | 139,183 |
| Forks | 20,454 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 60.1098341976053/100 |
| Open Issues | 17 |
| Last Updated | 2026-09-20 |
| Created | 2024-04-29 |
| Platforms | python |
| Est. Tokens | ~19k |
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awesome-llm-apps is 100+ AI Agents, Agent Skills and RAG Apps - Free and Open Source.. It is categorized as a Agent Tool with 139.2k GitHub stars.
awesome-llm-apps is primarily written in Python. It covers topics such as agents, llms, python.
You can find installation instructions and usage details in the awesome-llm-apps GitHub repository at github.com/Shubhamsaboo/awesome-llm-apps. The project has 139.2k stars and 20454 forks, indicating an active community.
awesome-llm-apps is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to awesome-llm-apps on Agent Skills Hub include mindsdb, open-webui, headroom. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: